1. Field of the Invention
The present invention is directed to the field of computer-implemented multidimensional database information management. It is more particularly directed at efficiently updating multidimensional database information.
2. Description of the Background Art
A computer-implemented database is a collection of data, organized in the form of tables. A table typically consists of columns that represent data of the same nature, and records that represent specific instances of data associated with the table. A relational database is a database that may be a set of tables containing information that is manipulated in accordance with the relational model associated with the data. For example, the product marketed under the trademarks IBM DB2 stores the data associated with the database in tables, and each table has a name. It will be appreciated that other vendors provide relational databases.
On-Line Analytical Processing (OLAP) is a computing technique for summarizing, consolidating, viewing, analyzing, applying formulae to, and synthesizing data according to multiple dimensions. OLAP software enables users, such as analysts, managers, and executives, to gain insight into performance of an enterprise, such as a corporation, through rapid access to a wide variety of data dimensions that are organized to reflect the multidimensional nature of enterprise data, typically by means of hypotheses about possible trends in the data. More particularly, OLAP techniques may be used to analyze data from different viewpoints by identifying interesting associations in the information in a database. Therefore, OLAP is a decision support technique used in data management for the purpose of modeling, analyzing, and reporting information, such as business information.
An increasingly popular data model for OLAP applications is the multidimensional database (MDDB). Often, data analysts use MDDBs during interactive exploration of data for finding regions of anomalies in the data. Before this data can be explored, data modeling needs to be enabled. Modeling data for an OLAP application may require large amounts of metadata, including data entities that may manage the associations between the data. The terms “data cube,” “multidimensional database,” “multidimensional cube,” and “cube” will be used interchangeably herein.
Hierarchy dependency relationships are present in dimension tables of the multidimensional database and are used to identify interesting associations in the information in the database. Often the hierarchy dependency relationships are used to manage the aggregate operations of multidimensional data. As incremental changes are made to the multidimensional data at a base transactional level, the associated aggregated data must be updated to ensure that the multidimensional information at various levels is accurate. For example, when multidimensional data at the base level is changed via a data transaction, the aggregated data at higher levels must also be refreshed or changed to ensure consistency of the multidimensional database information. This refresh typically involves accessing the metadata that manages the hierarchy dependency relationships between the multidimensional data and recomputing multidimensional aggregate values at various levels. Since each multidimensional database may have hundreds of aggregate tables and each aggregate table may have millions of rows, accessing metadata and refreshing aggregate data is often time consuming and adds increased cost to the techniques associated with analyzing multidimensional database information.
It would be useful for data analysts to be able to change multidimensional database data without incurring the high cost associated with a refresh of the associated data. From the foregoing it will be apparent that there is still a need to improve the techniques associated with refreshing multidimensional database information when some of the multidimensional data has been changed.